Google's AI "co-scientist" is based on the firm's Gemini large language models Google has unveiled an experimental artificial intelligence system that "uses advanced reasoning to help scientists synthesize vast amounts of literature, generate novel hypotheses, and suggest detailed research plans", according to its press release. "The idea with [the] 'AI co-scientist' is to give scientists superpowers," says Alan Karthikesalingam at Google. The tool, which doesn't have an official name yet, builds on Google's Gemini large language models. When a researcher asks a question or specifies a goal – to find a new drug, say – the tool comes up with initial ideas within 15 minutes. Several Gemini agents then "debate" these hypotheses with each other, ranking them and improving them over the following hours and days, says Vivek Natarajan at Google. During this process, the agents can search the scientific literature, access databases and use tools such as Google's AlphaFold system for predicting the structure of proteins.

A group of scientists from across the U.S. claim to have created the first artificial intelligence capable of generating AI without human supervision. A Google artificial intelligence system gave patients more accurate diagnoses and provided better bedside manner than traditional doctors, a recent study by the tech giant found. Actors portraying patients, unaware whether they were texting with real doctors or Google's Articulate Medical Intelligence Explorer (AMIE) overall preferred how the AI handled their medical conditions, according to the study, which was published Jan. 11 on the scholarly distribution site arXiv. A panel of doctors, meanwhile, also found AMIE to be more accurate at diagnosing the patients than actual physicians. "To our knowledge, this is the first time that a conversational AI system has ever been designed optimally for diagnostic dialogue and taking the clinical history," Alan Karthikesalingam, a clinical research scientist at Google Health in London and a co-author of the study, told the scientific journal Nature on Friday.

In this paper, we provide a characterization of the expected value of monotone submodular set functions with $n$ pairwise independent random inputs. Inspired by the notion of ``correlation gap'', we study the ratio of the maximum expected value of a function with arbitrary dependence among the random inputs with given marginal probabilities to the maximum expected value of the function with pairwise independent random inputs and the same marginal probabilities. Our results show that the ratio is upper bounded by: (a) $4/3$ for $n = 3$ with general marginal probabilities and any monotone submodular set function (b) $4/3$ for general $n$ with small and large marginal probabilities and any monotone submodular set function and (c) $4k/(4k-1)$ for general $n$, general identical probabilities and rank functions of $k$-uniform matroids. The bound is tight in all three cases. This contrasts with the $e/(e-1)$ bound on the correlation gap ratio for monotone submodular set functions with mutually independent random inputs (which is known to be tight in case (b)), and illustrates a fundamental difference in the behavior of submodular functions with weaker notions of independence. These results can be immediately extended beyond pairwise independence to correlated random inputs. We discuss applications in distributionally robust optimization and mechanism design and end the paper with a conjecture.

Family doctors already have patients turning to'Dr Google' for a diagnosis. But Google has now developed AI which could perform as well as a doctor when answering questions about ailments. The tech giant reports in the journal, Nature, that its latest model, which processes language similarly to ChatGPT, can answer a range of medical questions with 92.6 per cent accuracy. That is on a par with the answers provided by nine doctors from the UK, US and India, who were asked to respond to the same 80 questions. Researchers at Google say the technology does not threaten the jobs of GPs. Google has now developed AI which could perform as well as a doctor when answering questions about ailments.

Large language models show impressive results on few-shot NLP tasks. However, these models are memory and computation-intensive. Meta-training allows one to leverage smaller models for few-shot generalization in a domain-general and task-agnostic manner; however, these methods alone results in models that may not have sufficient parameterization or knowledge to adapt quickly to a large variety of tasks. To overcome this issue, we propose meta-training with demonstration retrieval, where we use a dense passage retriever to retrieve semantically similar labeled demonstrations to each example for more varied supervision. By separating external knowledge from model parameters, we can use meta-training to train parameter-efficient models that generalize well on a larger variety of tasks. We construct a meta-training set from UnifiedQA and CrossFit, and propose a demonstration bank based on UnifiedQA tasks. To our knowledge, our work is the first to combine retrieval with meta-training, to use DPR models to retrieve demonstrations, and to leverage demonstrations from many tasks simultaneously, rather than randomly sampling demonstrations from the training set of the target task. Our approach outperforms a variety of targeted parameter-efficient and retrieval-augmented few-shot methods on QA, NLI, and text classification tasks (including SQuAD, QNLI, and TREC). Our approach can be meta-trained and fine-tuned quickly on a single GPU.

Web applications, stored on remote servers and delivered over the Internet, allow organisations to carry out tasks without the need to install products locally, while reducing costs. An array of different types of web application is currently available on the market, but one technology that's really proved disruptive in this space is artificial intelligence. Capable of automating manual tasks, AI can lead to smarter decision-making using web applications, speed up operations, and bring other business benefits. Matthijs Aler, CEO of Ohpen, believes the value that can be driven by artificial intelligence in web applications "is highly dependent on the type of web application". He said: "You can turn any successful machine learning model into a web application (such as Google Lens or Google Translate). At the moment the big breakthroughs are related to images and language, so web applications processing these are the most likely to be a valuable use case."

Manipulation of biomedical images to misrepresent experimental results has plagued the biomedical community for a while. Recent interest in the problem led to the curation of a dataset and associated tasks to promote the development of biomedical forensic methods. Of these, the largest manipulation detection task focuses on the detection of duplicated regions between images. Traditional computer-vision based forensic models trained on natural images are not designed to overcome the challenges presented by biomedical images. We propose a multi-scale overlap detection model to detect duplicated image regions. Our model is structured to find duplication hierarchically, so as to reduce the number of patch operations. It achieves state-of-the-art performance overall and on multiple biomedical image categories.

This article will explore how artificial intelligence in web applications has been helping organisations drive value. Web applications, stored on remote servers and delivered over the Internet, allow organisations to carry out tasks without the need to install products locally, while reducing costs. An array of different types of web application is currently available on the market, but one technology that's really proved disruptive in this space is artificial intelligence. Capable of automating manual tasks, AI can lead to smarter decision-making using web applications, speed up operations, and bring other business benefits. Matthijs Aler, CEO of Ohpen, believes the value that can be driven by artificial intelligence in web applications "is highly dependent on the type of web application".

Designing good heuristic functions for graph search requires adequate domain knowledge. It is often easy to design heuristics that perform well and correlate with the underlying true cost-to-go values in certain parts of the search space but these may not be admissible throughout the domain thereby affecting the optimality guarantees of the search. Bounded suboptimal search using several of such partially good but inadmissible heuristics was developed in Multi-Heuristic A* (MHA*). Although MHA* leverages multiple inadmissible heuristics to potentially generate a faster suboptimal solution, the original version does not improve the solution over time. It is an one shot algorithm that requires careful setting of inflation factors to obtain a desired one time solution. In this work, we tackle this issue by extending MHA* to an anytime version that finds a feasible suboptimal solution quickly and continually improve it until time runs out. Our work is inspired from the Anytime Repairing A* (ARA*) algorithm. We prove that our precise adaptation of ARA* concepts in the MHA* framework preserves the original suboptimal and completeness guarantees and enhances MHA* to perform in an anytime fashion. Furthermore, we report the performance of A-MHA* in 3-D path planning domain and sliding tiles puzzle and compare against MHA* and other anytime algorithms.

Money can't buy you happiness (although you can reportedly lease it for a while). It definitely cannot buy you love. And the rumor is money also cannot buy you large troves of labeled data that are ready to be plugged into your particular AI use case, much to the chagrin of former Apple product manager Ivan Lee. "I spent hundreds of millions of dollars at Apple gathering labeled data," Lee said. "And even with its resources, we were still using spreadsheets."